IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v551y2020ics0378437119322642.html
   My bibliography  Save this article

Detecting communities in social networks based on cliques

Author

Listed:
  • Nedioui, Med Abdelhamid
  • Moussaoui, Abdelouahab
  • Saoud, Bilal
  • Babahenini, Mohamed Chaouki

Abstract

Social network analysis is an important tool that can be used in many domains. Among the social network analysis algorithms and tools we find the community structure detection. Many community structure detection algorithms have been developed over years, but most of them have a high computational complexity. In this paper we propose a new approach to find a community structure in networks. Our approach is more stable, accurate and effective to find the community structure in networks with high inter-community links. Our method operates in two phases. In the first phase, our method finds all the circuits in order to split the network into small elementary groups. Then (in the second phase)the community structure will be found by merging iteratively the different sub graphs resulting from the first phase by a fusion principle used in clique-based methods. Our method was evaluated on different types of networks. We tested our method on generated-computer and real-world networks. A comparison was held between our method and some known methods of community detection. The results show that our method is very effective in finding communities in networks and saving the time complexity.

Suggested Citation

  • Nedioui, Med Abdelhamid & Moussaoui, Abdelouahab & Saoud, Bilal & Babahenini, Mohamed Chaouki, 2020. "Detecting communities in social networks based on cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    • Handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437119322642
    DOI: 10.1016/j.physa.2019.124100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119322642
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2019.124100?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    2. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    3. Shen, Huawei & Cheng, Xueqi & Cai, Kai & Hu, Mao-Bin, 2009. "Detect overlapping and hierarchical community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1706-1712.
    4. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Renaud Fabre & Otmane Azeroual & Patrice Bellot & Joachim Schöpfel & Daniel Egret, 2022. "Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs," Future Internet, MDPI, vol. 14(9), pages 1-18, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    2. Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. Saoud, Bilal & Moussaoui, Abdelouahab, 2018. "A new hierarchical method to find community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 418-426.
    4. Zhang, Weitong & Zhang, Rui & Shang, Ronghua & Li, Juanfei & Jiao, Licheng, 2019. "Application of natural computation inspired method in community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 130-150.
    5. Cui, Yaozu & Wang, Xingyuan & Eustace, Justine, 2014. "Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 198-207.
    6. Wu, Zhihao & Lin, Youfang & Wan, Huaiyu & Tian, Shengfeng & Hu, Keyun, 2012. "Efficient overlapping community detection in huge real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(7), pages 2475-2490.
    7. Wilhelm, Thomas & Hollunder, Jens, 2007. "Information theoretic description of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(1), pages 385-396.
    8. Wu, Jianshe & Wang, Xiaohua & Jiao, Licheng, 2012. "Synchronization on overlapping community network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 508-514.
    9. Shang, Ronghua & Luo, Shuang & Zhang, Weitong & Stolkin, Rustam & Jiao, Licheng, 2016. "A multiobjective evolutionary algorithm to find community structures based on affinity propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 203-227.
    10. Badie, Reza & Aleahmad, Abolfazl & Asadpour, Masoud & Rahgozar, Maseud, 2013. "An efficient agent-based algorithm for overlapping community detection using nodes’ closeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(20), pages 5231-5247.
    11. Fu, Xianghua & Liu, Liandong & Wang, Chao, 2013. "Detection of community overlap according to belief propagation and conflict," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 941-952.
    12. Hao Xu & Yuan Ran & Junqian Xing & Li Tao, 2023. "An Influence-Based Label Propagation Algorithm for Overlapping Community Detection," Mathematics, MDPI, vol. 11(9), pages 1-17, May.
    13. Wu, Tao & Guo, Yuxiao & Chen, Leiting & Liu, Yanbing, 2016. "Integrated structure investigation in complex networks by label propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 68-80.
    14. Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    15. Hailu Yang & Deyun Chen & Guanglu Sun & Xiaoyu Ding & Yu Xin, 2019. "CC 2 : Defending Hybrid Worm on Mobile Networks with Two-Dimensional Circulation Control," Complexity, Hindawi, vol. 2019, pages 1-19, December.
    16. Zhong, Weiqiong & An, Haizhong & Shen, Lei & Fang, Wei & Gao, Xiangyun & Dong, Di, 2017. "The roles of countries in the international fossil fuel trade: An emergy and network analysis," Energy Policy, Elsevier, vol. 100(C), pages 365-376.
    17. Kim, Paul & Kim, Sangwook, 2015. "Detecting overlapping and hierarchical communities in complex network using interaction-based edge clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 46-56.
    18. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    19. Li Wang & Jiang Wang & Yuanjun Bi & Weili Wu & Wen Xu & Biao Lian, 2014. "Noise-tolerance community detection and evolution in dynamic social networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 600-612, October.
    20. Julio I. Chapeton & John H. Wittig & Sara K. Inati & Kareem A. Zaghloul, 2022. "Micro-scale functional modules in the human temporal lobe," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437119322642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.